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The next generation of driver assistance systems and highly automated driving functions are based on digital maps. In order to meet the high requirements on the correctness and up-to-dateness of this information, this work presents new automated methods to extract up-to-date map information from fleet data. The focus is on the inference of static intersection information from fleet data through machine learning and statistical methods.

Maschinelles Lernen --- Flottendaten --- Intersection Information --- Lichtsignalanlage --- Automated Map Creation --- Fleet Data --- Traffic Light --- Automatisierte Kartenerstellung --- Kreuzungsinformationen --- Machine Learning